Baselight

HR Employee Attrition

to find and filter the criteria which are most responsible for attrition

@kaggle.itssuru_hr_employee_attrition

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About this Dataset

HR Employee Attrition

Problem Statement

HR Analytics helps us with interpreting organizational data. It finds out the people-related trends in the data and helps the HR Department take the appropriate steps to keep the organization running smoothly and profitably. Attrition is a corporate setup is one of the complex challenges that the people managers and the HRs personnel have to deal with.

Interestingly, machine learning models can be deployed to predict potential attrition cases, thereby helping the appropriate HR Personnel take the necessary steps to retain the employee.

What you have to do…

Perform exploratory data analysis to find a pattern or find and filter the criteria which are most responsible for attrition.

Tables

Hr Employee Attrition

@kaggle.itssuru_hr_employee_attrition.hr_employee_attrition
  • 71.09 KB
  • 1470 rows
  • 35 columns
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CREATE TABLE hr_employee_attrition (
  "age" BIGINT,
  "attrition" VARCHAR,
  "businesstravel" VARCHAR,
  "dailyrate" BIGINT,
  "department" VARCHAR,
  "distancefromhome" BIGINT,
  "education" BIGINT,
  "educationfield" VARCHAR,
  "employeecount" BIGINT,
  "employeenumber" BIGINT,
  "environmentsatisfaction" BIGINT,
  "gender" VARCHAR,
  "hourlyrate" BIGINT,
  "jobinvolvement" BIGINT,
  "joblevel" BIGINT,
  "jobrole" VARCHAR,
  "jobsatisfaction" BIGINT,
  "maritalstatus" VARCHAR,
  "monthlyincome" BIGINT,
  "monthlyrate" BIGINT,
  "numcompaniesworked" BIGINT,
  "over18" VARCHAR,
  "overtime" VARCHAR,
  "percentsalaryhike" BIGINT,
  "performancerating" BIGINT,
  "relationshipsatisfaction" BIGINT,
  "standardhours" BIGINT,
  "stockoptionlevel" BIGINT,
  "totalworkingyears" BIGINT,
  "trainingtimeslastyear" BIGINT,
  "worklifebalance" BIGINT,
  "yearsatcompany" BIGINT,
  "yearsincurrentrole" BIGINT,
  "yearssincelastpromotion" BIGINT,
  "yearswithcurrmanager" BIGINT
);

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